Relevant bibliographies by topics / Incremental KNN

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Incremental KNN'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Incremental KNN"

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Yu, Cui, Rui Zhang, Yaochun Huang, and Hui Xiong. "High-dimensional kNN joins with incremental updates." GeoInformatica 14, no. 1 (2009): 55–82. http://dx.doi.org/10.1007/s10707-009-0076-5.

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Ajjaj, Souad, Souad El Houssaini, Mustapha Hain, and Mohammed-Alamine El Houssaini. "Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring." Telecom 4, no. 3 (2023): 629–48. http://dx.doi.org/10.3390/telecom4030028.

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Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machi
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Huo, Dexuan, Jilin Zhang, Xinyu Dai, et al. "A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition." Sensors 23, no. 5 (2023): 2433. http://dx.doi.org/10.3390/s23052433.

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The sensitivity and selectivity profiles of gas sensors are always changed by sensor drifting, sensor aging, and the surroundings (e.g., temperature and humidity changes), which lead to a serious decline in gas recognition accuracy or even invalidation. To address this issue, the practical solution is to retrain the network to maintain performance, leveraging its rapid, incremental online learning capacity. In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-incremental learning, and can be re
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Zaki, Ahmed Mohamed, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, and El-Sayed M. El El-Kenawy. "Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization." Journal of Cybersecurity and Information Management 13, no. 1 (2024): 76–84. http://dx.doi.org/10.54216/jcim.130108.

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The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation of computers, especially in predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance of the K-Nearest Neighbors (KNN) algorithm. The proposed methodology initiates with the establishment of a particle population, dynamically adjusting their positions and velocities and integrating a diffusi
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ZHAO, GENG, KEFENG XUAN, DAVID TANIAR, and BALA SRINIVASAN. "INCREMENTAL K-NEAREST-NEIGHBOR SEARCH ON ROAD NETWORKS." Journal of Interconnection Networks 09, no. 04 (2008): 455–70. http://dx.doi.org/10.1142/s0219265908002382.

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Most query search on road networks is either to find objects within a certain range (range search) or to find K nearest neighbors (KNN) on the actual road network map. In this paper, we propose a novel query, that is, incremental k nearest neighbor (iKNN). iKNN can be defined as given a set of candidate interest objects, a query point and the number of objects k, find a path which starts at the query point, goes through k interest objects and the distance of this path is the shortest among all possible paths. This is a new type of query, which can be used when we want to visit k interest objec
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Li, Huiqin, Yanling Li, Chuan He, Hui Zhang, and Jianwei Zhan. "Radar Working State Recognition Based on the Unsupervised and Incremental Method." Journal of Sensors 2021 (October 7, 2021): 1–14. http://dx.doi.org/10.1155/2021/8673046.

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Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm
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Patel, Damodar, and Amit Kumar Saxena. "Feature Selection in High Dimension Datasets using Incremental Feature Clustering." Indian Journal Of Science And Technology 17, no. 32 (2024): 3318–26. http://dx.doi.org/10.17485/ijst/v17i32.2077.

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Objectives: To develop a machine learning-based model to select the most important features from a high-dimensional dataset to classify the patterns at high accuracy and reduce their dimensionality. Methods: The proposed feature selection method (FSIFC) forms and combines feature clusters incrementally and produces feature subsets each time. The method uses K-means clustering and Mutual Information (MI) to refine the feature selection process iteratively. Initially, two clusters of features are formed using K-means clustering (K=2) by taking features as the basis of clustering instead of takin
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Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4077–91. https://doi.org/10.11591/ijece.v9i5.pp4077-4091.

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Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work presents a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by
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Chen, Jiusheng, Xingkai Xu, and Xiaoyu Zhang. "Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine." Journal of Electrical and Computer Engineering 2020 (August 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/9898546.

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Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient tra
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Damodar, Patel, and Kumar Saxena Amit. "Feature Selection in High Dimension Datasets using Incremental Feature Clustering." Indian Journal of Science and Technology 17, no. 32 (2024): 3318–26. https://doi.org/10.17485/IJST/v17i32.2077.

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Abstract <strong>Objectives:</strong>&nbsp;To develop a machine learning-based model to select the most important features from a high-dimensional dataset to classify the patterns at high accuracy and reduce their dimensionality.&nbsp;<strong>Methods:</strong>&nbsp;The proposed feature selection method (FSIFC) forms and combines feature clusters incrementally and produces feature subsets each time. The method uses K-means clustering and Mutual Information (MI) to refine the feature selection process iteratively. Initially, two clusters of features are formed using K-means clustering (K=2) by t
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Dissertations / Theses on the topic "Incremental KNN"

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(6636224), Seunghee Lee. "Incremental Support Vector Machine Approach for DoS and DDoS Attack Detection." Thesis, 2019.

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<div> <div> <div> <p>Support Vector Machines (SVMs) have generally been effective in detecting instances of network intrusion. However, from a practical point of view, a standard SVM is not able to handle large-scale data efficiently due to the computation complexity of the algorithm and extensive memory requirements. To cope with the limitation, this study presents an incremental SVM method combined with a k-nearest neighbors (KNN) based candidate support vectors (CSV) selection strategy in order to speed up training and test process. The proposed incremental SVM method constructs or updates
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Book chapters on the topic "Incremental KNN"

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Fu, JuiHsi, and SingLing Lee. "A Multi-Strategy Approach to KNN and LARM on Small and Incrementally Induced Prediction Knowledge." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03348-3_44.

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Conference papers on the topic "Incremental KNN"

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Huang, Xiaohua, and Wenming Zheng. "Effective discriminative TCM-KNN for incremental learning." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Mingyue Ding, Bir Bhanu, Friedrich M. Wahl, and Jonathan Roberts. SPIE, 2009. http://dx.doi.org/10.1117/12.832567.

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Forster, Kilian, Samuel Monteleone, Alberto Calatroni, Daniel Roggen, and Gerhard Troster. "Incremental kNN Classifier Exploiting Correct-Error Teacher for Activity Recognition." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.72.

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Jie, LI, Xue Yaxu, and Yu Yadong. "Incremental Learning Algorithm of Data Complexity Based on KNN Classifier." In 2020 International Symposium on Community-centric Systems (CcS). IEEE, 2020. http://dx.doi.org/10.1109/ccs49175.2020.9231514.

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Zhong, Xueyan, Long Chen, Zhongyang Han, Jun Zhao, and Wei Wang. "Industrial Time Series Prediction Based on Incremental DBSCAN-KNN with Self-learning Scheme." In 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2023. http://dx.doi.org/10.1109/ddcls58216.2023.10165826.

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Donat, William, Kihoon Choi, Woosun An, Satnam Singh, and Krishna Pattipati. "Data Visualization, Data Reduction and Classifier Fusion for Intelligent Fault Detection and Diagnosis in Gas Turbine Engines." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-28343.

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In this paper, we investigate four key issues associated with data-driven approaches for fault classification using the Pratt and Whitney commercial dual-spool turbofan engine data as a test case. The four issues considered here include: (1) Can we characterize, a priori, the difficulty of fault classification via self-organizing maps? (2) Do data reduction techniques improve fault classification performance and enable the implementation of data-driven classification techniques in memory-constrained digital electronic control units (DECUs)?, (3) When does adaptive boosting, an incremental fusi
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Liu, Mengxi, Sizhen Bian, Bo Zhou, and Paul Lukowicz. "iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets." In UbiComp '24: The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2024. http://dx.doi.org/10.1145/3675095.3676618.

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Abugharara, Abdelsalam, and Stephen Butt. "Investigation of the Relation Between Coring Parameters and Formation Representation." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-105315.

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Abstract Coring is an essential operation for subsurface formation evaluation. It is a key to recover cores from formation and have them unaltered for best formation representation. Numerous reasons could influence core integrity that could produce data through core characterization that could misrepresent the formation. The aim of this paper is to investigate the influence of the (input vs. output) coring parameters and coring bit type on the coring performance and the cored formation property representation in high strength rocks. Such aim was investigated through coring operations applying
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Somaki, Takahiro, Tsuyoshi Fukasawa, Takayuki Miyagawa, et al. "Research and Development of Three-Dimensional Isolation System for Sodium-Cooled Fast Reactor: Part 2 — Details of Characteristics for Disc Springs and Oil Dampers on Basis of Full-Scale Loading Tests." In ASME 2018 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/pvp2018-84491.

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Being compatible with the seismic and thermal loads for the large Sodium-cooled Fast Reactor (SFR), the three-dimensional isolation system is inevitable technology. The three-dimensional isolation system consists of the thick rubber bearings, the disc springs and the oil dampers. Since the isolation performances on the rubber bearings in the horizontal direction have been revealed by the previous studies [1], the vertical isolation performance and characteristics such as restoring force and damping performance should be clarified by loading tests to build the analytical model. This paper prese
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